Related papers: Generating robust counterfactual explanations

Generating robust counterfactual explanations

URL: http://arxiv.org/abs/2304.12943v1
Date: Mon, 24 Apr 2023 09:00:31 GMT
Title: Generating robust counterfactual explanations
Authors: Victor Guyomard, Fran\c{c}oise Fessant, Thomas Guyet, Tassadit Bouadi, Alexandre Termier
Abstract summary: The quality of a counterfactual depends on several criteria: realism, actionability, validity, robustness, etc. In this paper, we are interested in the notion of robustness of a counterfactual. More precisely, we focus on robustness to counterfactual input changes. We propose a new framework, CROCO, that generates robust counterfactuals while managing effectively this trade-off, and guarantees the user a minimal robustness.
Score: 60.32214822437734
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Counterfactual explanations have become a mainstay of the XAI field. This particularly intuitive statement allows the user to understand what small but necessary changes would have to be made to a given situation in order to change a model prediction. The quality of a counterfactual depends on several criteria: realism, actionability, validity, robustness, etc. In this paper, we are interested in the notion of robustness of a counterfactual. More precisely, we focus on robustness to counterfactual input changes. This form of robustness is particularly challenging as it involves a trade-off between the robustness of the counterfactual and the proximity with the example to explain. We propose a new framework, CROCO, that generates robust counterfactuals while managing effectively this trade-off, and guarantees the user a minimal robustness. An empirical evaluation on tabular datasets confirms the relevance and effectiveness of our approach.

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